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Renal Cell Carcinoma (RCC) contributes significantly to the global cancer burden, yearly accounting for over 110,000 deaths worldwide. Several lifestyle risk factors have been strongly implicated in RCC aetiology, most notably obesity and hypertension, as well as other factors associated with the metabolic syndrome (MetS).[2,3] We have also identified an independent inverse association with risk of circulating vitamin B6 involved in the one-carbon metabolism (OCM) pathway, as well as specific genetic variants through genome-wide association studies (GWAS).[4-6] While these factors would appear robustly implicated in RCC aetiology in an epidemiological point of view, the underlying causal pathways remain to be elucidated.[2,3]
We propose to establish a comprehensive research programme focusing on metabolomic analyses of pre-diagnostically collected plasma samples. We argue that such an approach have a strong potential to identify novel biomarkers of RCC, and to substantially deepen our understanding of how already identified risk factors, such as MetS and OCM, are mediated in RCC pathogenesis. Our objectives are i) to quantify 150 metabolites in cryopreserved plasma for 700 prospectively collected RCC cases and individually matched controls, ii) to identify single metabolites associated with RCC risk, iii) to identify underlying metabolic profiles associated with RCC risk, and iv) to conduct a genetic analysis along the lines of Mendelian randomization using data available from a parallel GWAS.
A targeted metabolomics platform will be used to quantify 150 metabolites using the well-proven Biocrates AbsoluteIDQ (TM) p180 kit in pre-diagnostic plasma samples from 700 RCC cases and individually matched controls nested within three prospective cohorts (EPIC, MCCS, and NSHDC). After the quality control checks, conditional logistic regression will be used to i) identify individual metabolites associated with RCC risk (accounting for multiple comparisons) and ii) assess the extent to which metabolites associated with risk are independent of previously identified risk factors mentioned above. Subsequently, we will iii) apply data driven tools, such as PLS-DA to identify metabolic profiles that may indicate underlying disease pathways. Finally, iv) a genome-wide analysis will be conducted using data available on all research participants from a parallel GWAS, as well as a study of 10,000 case-control pairs, the aim being to link gene-variants to RCC associated metabolites, as well as to RCC risk. This line of research may provide evidence on causality along the lines of Mendelian randomization.
While the incidence rates for other smoking related cancer, such as lung cancer, have decreased substantially in countries following declines in smoking prevalence, RCC have not benefitted from these trends. In the contrary, several countries have experienced notable increases in RCC rates over the last decades,[2,3] highlighting the importance of clarifying other etiological factors of RCC, in particular those related to obesity and hypertension. We believe that taking advantage of recent technological advances in metabolomics, and applying such techniques on well-characterized study populations has the potential of substantially accelerate our understanding of RCC, and ultimately provide evidence for primary prevention.
Several risk factors of renal cell carcinoma (RCC) have been convincingly identified. While tobacco smokers experience a slight risk increase, other more important lifestyle factors influencing RCC risk are obesity and hypertension. We have also identified additional blood biomarkers of vitamin B metabolism that further modify RCC risk. However, the biological mechanisms in regards to how the above-mentioned risk factors affect RCC are yet to be explained.
We propose to establish an ambitious research programme with a focus on novel metabolic profiling techniques applied to blood plasma. The overarching aim of this project is to provide a better understanding of the metabolic processes by which obesity, hypertension and other associated conditions, as well as B-vitamins, influence the risk to develop RCC. In particular, we aim to identify individual metabolites associated with RCC and evaluate if they can explain the mechanisms by which other lifestyle factors affect risk. We also aim to identify metabolic profiles by combining multiple metabolites that may jointly provide a better understanding to the metabolic pathways underlying RCC development. Finally, available genetic information will be used to further characterize any identified risk metabolites with respect to RCC.
We will analyse blood plasma samples from RCC patients donated up to 15 years before receiving their diagnosis, and compare them with paired individuals who remained cancer-free, using a nested case-control study design. In total 700 RCC patients and comparable controls will be included from three separate cohort studies. We will use a targeted metabolomics approach to measure concentrations of 150 individual metabolites. Standard statistical methods will be used to compare the RCC cases and controls in order to identify individual metabolites linked to risk of the disease, and evaluate if they can explain mechanisms underlying associations between previously identified risk factors and RCC. We will also use more advanced statistical procedures to identify metabolic profiles the may indicate broader disease pathways and mechanisms.
The genetic analysis will make use of data on hundreds of thousands of genetic variants that are available in the context of a parallel study on the same samples. Because gene variants cannot be affected by lifestyle related risk factors, they can be used to further clarify the link between metabolites and disease risk. For any metabolite associated with risk, we will therefore try to identify genetic variants that relate to such a metabolite, and then evaluate the relation between the gene variant and RCC.
RCC has become more common in several countries over the last decades, highlighting the importance of providing a better understanding of why some lifestyle factors influence risk, in particular those related to obesity and hypertension. In this context, we believe that taking advantage of recent developments in metabolomics in large and well-characterized study populations has a strong potential, and may provide useful information for RCC prevention.